See also the GSBS Web site for G. Balázsi.
Mathematical/computational modeling and experimental characterization of biomolecular interaction networks to unravel molecular mechanisms underlying cellular survival in stress.
Studying the combined effect of noise and feedback regulation on the development of drug resistance by experiment and computational modeling. Our earlier studies proved that noise can aid survival after a single exposure to stress. The current project will test the effect of feedback regulation on the development and maintenance of non-genetic drug resistance. We will apply multiple exposures to stress, testing how a cell population benefits from the "memory" of earlier stress events due to positive autoregulation.
Designing gene constructs to shape the distribution of protein levels within a cell population. For example, we can now independently adjust the mean and noise (Coefficient of Variation) of a target gene in yeast. We have also built a "linearizer" gene circuit that converts a nonlinear (sigmoidal) dose response to linear.
Identifying the network topology around stress-related genes within large-scale gene regulatory networks of three organisms: E. coli, S. cerevisiae and H. sapiens. We have discovered a distinct pattern of positioning and regulation of stress-related genes that is similar across the kingdoms of life, suggesting that it emerged due to similar evolutionary driving forces acting on all forms of life.
Studying the response of the large-scale gene regulatory networks of infectious microbes to stress using published microarray data. We identify distinct sets of transcriptional subnetworks (origons) that are affected at various times following exposure to stress. These results open the door for a systems-level understanding of the response of infectious microbes to stress, as well as their drug tolerance or drug resistance.
Analyzing and interpreting the large-scale proteomics/drug screening/siRNA data collected at our department in the Gordon Mills laboratory. We are inferring signaling networks based on experimental data, and study their overlap with known interaction networks.
Supporting Data & Software
Gabor Balazsi, Ph.D.
CHIP Lab, Department of Systems Biology (Unit 950)
The University of Texas MD Anderson Cancer Center
7435 Fannin Street
Houston, TX 77054, USA
Phone & Fax